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Record W2895331643

Simulation of Fluid forces in Fuel Bundles

2018· article· en· W2895331643 on OpenAlexaff
Alexander Moksyakov, Osama Elbanhawy, Marwan Hassan

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicVibration and Dynamic Analysis
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsInertiaVibrationMechanicsFrettingStiffnessTube (container)Surface forceContact forceBundleStructural engineeringMaterials scienceEngineeringMechanical engineeringClassical mechanicsPhysicsComposite material
DOInot available

Abstract

fetched live from OpenAlex

Vibrations of fuel bundles are major concerns as they can result in fretting wear damage at the interior surface of the pressure tube.  The fretting damage takes place at the locations where the outer fuel elements come in contact with the pressure tube surface.  The fretting wear results from the impact and sliding motion of the fuel elements against the pressure tube surface.  In addition, structural problem in the form endplate cracking can take place due to these vibrations.  One of the fluid forces that can greatly affect the dynamics of the fuel bundles are generated by the motion of the fuel elements in axial flow.  The fluid force acting on any fuel element is assumed to be a linear combination of the fluid forces created by the motion of all surrounding fuel elements.   These forces are expressed in terms of force coefficients representing the influence of these motions and can be decomposed into inertia, stiffness, and damping components.   The numerical model is introduced the represent these forces in a flexible fuel kernel.  The central element was given a period motion and the forces acting on the surrounding elements were obtained.  The obtained force coefficients were used in an analytical framework to predict the fuel bundle vibrations.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.677
Threshold uncertainty score0.517

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.009
GPT teacher head0.233
Teacher spread0.224 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2018
Admission routes1
Has abstractyes

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